A mutual local-ternary-pattern based method for aligning differently exposed images

Saturation and large intensity variations occurred in multi-exposed images offer great challenges to align these images. In this paper, a mutual local-ternary-pattern (MLTP) is proposed to represent differently exposed images for image registration. Different from the classical local ternary pattern (LTP) and its variants, the proposed MLTP has two salient properties: (1) The ternary pattern of one image is not only determined by itself, but also relied on its counterpart; (2) The MLTP is grayscale-adaptive. It is analyzed that the proposed MLTP is a good representation to preserve consistency of differently exposed images. Based on the MLTP-coded images, an efficient linear model derived from Taylor expansion is presented to estimate motion parameters. To improve accuracy and efficiency, image rotation is initially detected by the histogram-based matching, and coarse-to-fine technique is implemented to cope with possibly large movement. Extensive experiments carried out on a variety of synthesized and real multi-exposed images demonstrate that the proposed method is robust to 10 exposure values (EV), which is superior to other methods and current commercial HDR tools.